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Detection of Heart Rate Variability from Photoplethysmography (PPG) Signals Obtained by Raspberry Pi Microcomputer

Yıl 2022, Cilt: 5 Sayı: 1, 104 - 120, 30.04.2022

Ziynet Pamuk , Ceren Kaya

https://doi.org/10.35377/saucis...1024414

Öz

Photoplethysmography (PPG) signals are signals obtained as a result of optically measuring volumetric changes in capillaries. Volumetric changes in capillaries also depend on the work of heart. According to recent researches, it has been seen that PPG signals contain a lot of information about the physiological and biological state of related person. Most of these studies are based on the analysis of characteristics and waveforms of PPG signals obtained with a single wavelength in time and frequency domains. In this study, 10 minutes of data was taken from the left index finger of a 24-year-old male, which was positioned horizontally using a MAX30100 sensor and Raspberry Pi 4 microprocessor kit. Experiments are carried out in the fully resting state of a male volunteer in outdoors and stressful environments. While the MAX30100 sensor shows the heartbeat on the screen, it also gives PPG signal data, which is a single wavelength, into a .csv file as received data. In these cases, five different time domain parameters of received PPG signals are extracted. When the results are interpreted, it is seen that all results are meaningful and consistent.

Anahtar Kelimeler

photoplethysmography heart rate variability time domain parameters raspberry pi

Destekleyen Kurum

ZONGULDAK BÜLENT ECEVİT ÜNİVERSİTESİ

Proje Numarası

2020-39971044-02

Teşekkür

This research has been supported by Scientific Research Projects Commission of Zonguldak Bulent Ecevit University, Turkey, under Project Number: 2020-39971044-02. We would also like to thank Abdelgader Obeida, Dilan Pişkin and İhsan Acuz for valuable contribution to the study.

Kaynakça

  • [1] E. E. Benarroch, "The central autonomic network: functional organization, dysfunction, and perspective," In Mayo Clinic Proceedings, vol. 68, no. 10, pp. 988-1001, 1993.
  • [2] J. V. Freeman, F. E. Dewey, D. M. Hadley, J. Myers and V. F. Froelicher, "Autonomic nervous system interaction with the cardiovascular system during exercise," Progress in Cardiovascular Diseases, vol. 48, no. 5, pp. 342-362, 2006.
  • [3] J. E. Hall, Guyton ve Hall Tıbbi Fizyoloji. İstanbul, Güneş Tıp Kitabevi, 2017.
  • [4] M. K. Lahiri, P. J. Kannankeril and J. J. Goldberger, "Assessment of autonomic function in cardiovascular disease: physiological basis and prognostic implications," Journal of the American college of Cardiology, vol. 51, no. 18, pp. 1725-1733, 2008.
  • [5] A. D. Jose and F. Stitt, "Effects of hypoxia and metabolic inhibitors on the intrinsic heart rate and myocardial contractility in dogs," Circulation research, vol. 25, no. 1, pp. 53-66, 1969.
  • [6] A. D. Jose, F. Stitt and D. Collison, "The effects of exercise and changes in body temperature on the intrinsic heart rate in man," American heart journal, vol. 79, no. 4, pp. 488-498, 1970.
  • [7] E. H. Hon, " Electronic evaluations of the fetal heart rate patterns preceding fetal death, further observations," Am J Obstet Gynecol, vol. 87, no. 1, pp. 814-826, 1965.
  • [8] H. V. Huikuri and P. K. Stein, " Clinical application of heart rate variability after acute myocardial infarction," Frontiers in physiology, vol. 3, no. 41, pp. 1-5, 2012.
  • [9] C. S. Zuern, P. Barthel and A. Bauer, "Heart rate turbulence as risk-predictor after myocardial infarction," Frontiers in physiology, vol. 2, no. 99, pp. 1-8, 2011.
  • [10] H. Mølgaard, K. E. Sørensen and P. Bjerregaard, "Attenuated 24-h heart rate variability in apparently healthy subjects, subsequently suffering sudden cardiac death," Clinical Autonomic Research, vol. 1, no. 3, pp. 233-237, 1991.
  • [11] A. Pozzati, L. G. Pancaldi, G. Di Pasquale, G. Pinelli and R. Bugiardini,"Transient sympathovagal imbalance triggers “ischemic” sudden death in patients undergoing electrocardiographic Holter monitoring," Journal of the American College of Cardiology, vol. 27, no. 4, pp. 847-852, 1996.
  • [12] M. T. Kearney et al., " Predicting death due to progressive heart failure in patients with mild-to-moderate chronic heart failure," Journal of the American College of Cardiology, vol. 40, no. 10, pp. 1801-1808, 2002.
  • [13] M. Efeoğlu, "Acil Tıp Eğitimi İçin EKG, EKG Kütüphanesi," 2014. [Online]. Available: https://acilci.net/kategori/tibbi-kategoriler/kardiyoloji/ekg/litfl-ekg-kutuphanesi/. [Accessed: 14-Oct-2021].
  • [14] J. Nolan et al.,"Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart)," Circulation, vol. 98, no. 15, pp. 1510-1516, 1998.
  • [15] S. Guzzetti, R. Magatelli, E. Borroni and S. Mezzetti, "Heart rate variability in chronic heart failure," Autonomic neuroscience, vol. 90, no. 1-2, pp. 102-105, 2001.
  • [16] B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi and R. Bugiardini, "Heart rate variability today," Progress in cardiovascular diseases, vol. 55, no. 3, pp. 321-331, 2012.
  • [17] M. H. Asyali, "Discrimination power of long-term heart rate variability measures," Proc. - 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 200-203, 2003.
  • [18] P. Bušek, J. Vaňková, J. Opavský, J. Salinger and S. Nevšímalová, "Spectral analysis of heart rate variability in sleep," Physiol res, vol. 54, no. 4, pp. 369-376, 2005.
  • [19] M. T. La Rovere et al., " Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients," Circulation, vol. 107, no. 4, pp. 565-570, 2003.
  • [20] R. Brunelli and T. Poggio, “Face recognition: Features versus templates,” IEEE transactions on pattern analysis and machine intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.
  • [21] A. Samal and P. A. Iyengar, “Automatic recognition and analysis of human faces and facial expressions: A survey,” Pattern recognition, vol. 25, no. 1, pp. 65-77, 1992.
  • [22] D. Dumn, “Using a multi-layer perceptron neural for human voice identification,” Proc. - 4th Int. Conf. Signal Process. Applicat. Technol., 1993.
  • [23] L. Biel, O. Pettersson, L. Philipson and P. Wide, “ECG analysis: a new approach in human identification,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 3, pp. 808-812, 2001.
  • [24] S. Mayya, V. Jilla, V. N. Tiwari, M. M. Nayak and R. Narayanan, " Continuous monitoring of stress on smartphone using heart rate variability," Proc. - 15th IEEE International Conference on Bioinformatics and Bioengineering, pp. 1-5, 2015.
  • [25] G. Lu, F. Yang, J. A. Taylor and J. F. Stein, " A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects," Journal of medical engineering & technology, vol. 33, no. 8, pp. 634- 641, 2009.
  • [26] N. Selvaraj, A. Jaryal, J. Santhosh, K. K. Deepak and S. Anand, "Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography," Journal of medical engineering & technology, vol. 32, no. 6, pp. 479-484, 2008.
  • [27] J. Taelman, S. Vandeput, A. Spaepen ans S. Van Huffel, "Influence of mental stress on heart rate and heart rate variability," Proc. - 4th European conference of the international federation for medical and biological engineering, pp. 1366-1369, 2009.
  • [28] W. Wu and J. Lee, "Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment," Proc. - 10th IEEE International Symposium on Signal Processing and Information Technology, pp. 144-149, 2010.
  • [29] W. Wu, J. Lee and H. Chen, " Estimation of heart rate variability changes during different visual stimulations using non-invasive continuous ecg monitoring system,” Proc. - International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp. 344-347, 2009.
  • [30] D. McDuff, S. Gontarek and R. Picard, "Remote measurement of cognitive stress via heart rate variability," Proc. - 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2957-2960, 2014.
  • [31] C. Wang and F. Wang, "An emotional analysis method based on heart rate variability," Proc. - IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 104-107, 2012.
  • [32] P. B. Laursen, A. Said and B. Martin, "Nocturnal heart rate variability following supramaximal intermittent exercise," International Journal of Sports Physiology and Performance, vol. 4, no. 4, pp. 435-447, 2009.
  • [33] P. J. Millar, M. Rakobowchuk, N. Mccartney and M. J. Macdonald, " Heart rate variability and nonlinear analysis of heart rate dynamics following single and multiple Wingate bouts," Applied Physiology, Nutrition & Metabolism, vol. 34, no. 5, pp. 875-883, 2009.
  • [34] O. F. Barak, D. G. Jakovljevic, J. Z. P. Gacesa, Z. B. Ovcin, D. A. Brodie and N. G. Grujic, " Heart rate variability before and after cycle exercise in relation to different body positions," Journal of sports science & medicine, vol. 9, no. 2, pp. 176-182, 2010.
  • [35] U. Wiklund, M. Karlsson, M. Oöström and T. Messner, " Influence of energy drinks and alcohol on post‐exercise heart rate recovery and heart rate variability," Clinical physiology and functional imaging, vol. 29, no. 1, pp. 74-80, 2009.
  • [36] D. Aras, F. Akça and C. Akalan, "50 metre sprint yüzmenin 13-14 yaşlarındaki erkek yüzücülerde kalp hızı değişkenliğine etkisi," Spormetre Beden Eğitimi ve Spor Bilimleri Dergisi, vol. 11, no. 1, pp. 13-18, 2013.
  • [37] R. Ulu, N. Gözel, İ. P. Yiğit, Z. Kemeç, K. A. Uğur, O. Doğdu and A. Doğukan, " Diyaliz Modalitelerinin Kalp Hızı Değişkenliği Üzerine Olan Etkisi," Türk Nefroloji Diyaliz ve Transplantasyon Dergisi, vol. 26, no. 1, pp. 93-97, 2017.
  • [38] A. F. Aleixo, E. G. Lima, É. C. Leite, A. V. Inocêncio, L. T. Lins and M. A. Rodrigues, "Wearable Device for Acquisition of SpO 2 and Heart Rate," Proc. - XXVI Brazilian Congress on Biomedical Engineering, pp. 577-582, 2019.
  • [39] M. Paul, A. F. Mota, C: H. Antink, V. Blazek and S. Leonhardt, " Modeling photoplethysmographic signals in camera-based perfusion measurements: optoelectronic skin phantom," Biomedical optics express, vol. 10, no. 9, pp. 4353-4368, 2019.
  • [40] J. Přibil, A. Přibilová and I. Frollo, "Comparative Measurement of the PPG Signal on Different Human Body Positions by Sensors Working in Reflection and Transmission Modes," In Engineering Proceedings, vol. 2, no. 69, pp. 1-7, 2020.
  • [41] Ö. Yıldırım, Kalp aritmisinin çift dalgaboylu PPG sinyalleri kullanılarak belirlenmesi, Master Thesis, Dept. of Electrical and Electronics Engineering, Graduate Institute of Natural and Applied Sciences, Karadeniz Teknik University, Trabzon, Turkey, 2017.
  • [42] D. S. Goldstein, R. McCarty, R. J. Polinsky and I. J. Kopin, "Relationship between plasma norepinephrine and sympathetic neural activity," Hypertension, vol. 5, no. 4, pp. 552-559, 1983.
  • [43] B. G. Wallin and N. Charkoudian, " Sympathetic neural control of integrated cardiovascular function: insights from measurement of human sympathetic nerve activity," Muscle & nerve, vol. 36, no. 5, pp. 595-614, 2007.
  • [44] T. B. Kuo and C. C. Yang, "Altered frequency characteristic of central vasomotor control in SHR," American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 1, pp. H201-H207, 2000.
  • [45] A. H. Marques, M. N. Silverman and E. M. Sternberg, "Evaluation of stress systems by applying noninvasive methodologies: measurements of neuroimmune biomarkers in the sweat, heart rate variability and salivary cortisol," Neuroimmunomodulation, vol. 17, no. 3, pp. 205-208, 2010.
  • [46] R. Freeman and A. L. Komaroff, "Does the chronic fatigue syndrome involve the autonomic nervous system?" The American journal of medicine, vol. 102, no. 4, pp. 357-364, 1997.
  • [47] J. W. Hughes and C. M. Stoney, " Depressed mood is related to high-frequency heart rate variability during stressors," Psychosomatic medicine, vol. 62, no. 6, pp. 796- 803, 2000.
  • [48] T. Chandola, A. Heraclides and M. Kumari, " Psychophysiological biomarkers of workplace stressors," Neuroscience & Biobehavioral Reviews, vol. 35, no. 1, pp. 51-57, 2010.
  • [49] S. Açıkgöz and E. Diker, "Kalp hızı değişkenliği," MN Kardiyoloji, vol. 3, no. 1, pp. 275-278, 1996.
  • [50] "MAX30100 Nabız ve Kalp Atış Hızı Sensör Modülü," 2021. [Online]. Available: https://www.direnc.net/max30100-nabiz-ve-kalp-atis-hizi-sensor-modulu. [Accessed: 24-June-2021].
  • [51] "Maxim Integrated MAX30100 Datasheet," 2021. [Online]. Available: https://datasheets.maximintegrated.com/en/ds/MAX30100.pdf. [Accessed: 24-June-2021].
  • [52] R. A. Peura, "Chapter 7. Blood Pressure and Sound," 2014. [Online]. Available: http://slideplayer.com/slide/4156539/ [Accessed: 24-June-2021].
  • [53] D. Yılmaz and B. Çiğdem B, "Epilepsi Hastalarında Levetirasetam Tedavisinin Otonom Sinir Sistemi Fonksiyonları Üzerine Etkileri," Epilepsi, vol. 26, no. 2, pp. 81-87, 2020.
  • [54] K. L. Chong, D. Holden and T. Olin, "Heart Rate Monitor," 2010. [Online]. Available: http://www.academia.edu/7884606/Heart_Rate_Monitor [Accessed: 24-June-2021].
  • [55] G. Aydın and O. Özhan, "Pulse Oksimetre Tasarım Ve Analizinin Yapılması", Proc. - 2. Ulusal Biyomedikal Cihaz Tasarımı ve Üretimi Sempozyumu, pp. 46-48, 2017.

Yıl 2022, Cilt: 5 Sayı: 1, 104 - 120, 30.04.2022

Ziynet Pamuk , Ceren Kaya

https://doi.org/10.35377/saucis...1024414

Öz

Proje Numarası

2020-39971044-02

Kaynakça

  • [1] E. E. Benarroch, "The central autonomic network: functional organization, dysfunction, and perspective," In Mayo Clinic Proceedings, vol. 68, no. 10, pp. 988-1001, 1993.
  • [2] J. V. Freeman, F. E. Dewey, D. M. Hadley, J. Myers and V. F. Froelicher, "Autonomic nervous system interaction with the cardiovascular system during exercise," Progress in Cardiovascular Diseases, vol. 48, no. 5, pp. 342-362, 2006.
  • [3] J. E. Hall, Guyton ve Hall Tıbbi Fizyoloji. İstanbul, Güneş Tıp Kitabevi, 2017.
  • [4] M. K. Lahiri, P. J. Kannankeril and J. J. Goldberger, "Assessment of autonomic function in cardiovascular disease: physiological basis and prognostic implications," Journal of the American college of Cardiology, vol. 51, no. 18, pp. 1725-1733, 2008.
  • [5] A. D. Jose and F. Stitt, "Effects of hypoxia and metabolic inhibitors on the intrinsic heart rate and myocardial contractility in dogs," Circulation research, vol. 25, no. 1, pp. 53-66, 1969.
  • [6] A. D. Jose, F. Stitt and D. Collison, "The effects of exercise and changes in body temperature on the intrinsic heart rate in man," American heart journal, vol. 79, no. 4, pp. 488-498, 1970.
  • [7] E. H. Hon, " Electronic evaluations of the fetal heart rate patterns preceding fetal death, further observations," Am J Obstet Gynecol, vol. 87, no. 1, pp. 814-826, 1965.
  • [8] H. V. Huikuri and P. K. Stein, " Clinical application of heart rate variability after acute myocardial infarction," Frontiers in physiology, vol. 3, no. 41, pp. 1-5, 2012.
  • [9] C. S. Zuern, P. Barthel and A. Bauer, "Heart rate turbulence as risk-predictor after myocardial infarction," Frontiers in physiology, vol. 2, no. 99, pp. 1-8, 2011.
  • [10] H. Mølgaard, K. E. Sørensen and P. Bjerregaard, "Attenuated 24-h heart rate variability in apparently healthy subjects, subsequently suffering sudden cardiac death," Clinical Autonomic Research, vol. 1, no. 3, pp. 233-237, 1991.
  • [11] A. Pozzati, L. G. Pancaldi, G. Di Pasquale, G. Pinelli and R. Bugiardini,"Transient sympathovagal imbalance triggers “ischemic” sudden death in patients undergoing electrocardiographic Holter monitoring," Journal of the American College of Cardiology, vol. 27, no. 4, pp. 847-852, 1996.
  • [12] M. T. Kearney et al., " Predicting death due to progressive heart failure in patients with mild-to-moderate chronic heart failure," Journal of the American College of Cardiology, vol. 40, no. 10, pp. 1801-1808, 2002.
  • [13] M. Efeoğlu, "Acil Tıp Eğitimi İçin EKG, EKG Kütüphanesi," 2014. [Online]. Available: https://acilci.net/kategori/tibbi-kategoriler/kardiyoloji/ekg/litfl-ekg-kutuphanesi/. [Accessed: 14-Oct-2021].
  • [14] J. Nolan et al.,"Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart)," Circulation, vol. 98, no. 15, pp. 1510-1516, 1998.
  • [15] S. Guzzetti, R. Magatelli, E. Borroni and S. Mezzetti, "Heart rate variability in chronic heart failure," Autonomic neuroscience, vol. 90, no. 1-2, pp. 102-105, 2001.
  • [16] B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi and R. Bugiardini, "Heart rate variability today," Progress in cardiovascular diseases, vol. 55, no. 3, pp. 321-331, 2012.
  • [17] M. H. Asyali, "Discrimination power of long-term heart rate variability measures," Proc. - 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 200-203, 2003.
  • [18] P. Bušek, J. Vaňková, J. Opavský, J. Salinger and S. Nevšímalová, "Spectral analysis of heart rate variability in sleep," Physiol res, vol. 54, no. 4, pp. 369-376, 2005.
  • [19] M. T. La Rovere et al., " Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients," Circulation, vol. 107, no. 4, pp. 565-570, 2003.
  • [20] R. Brunelli and T. Poggio, “Face recognition: Features versus templates,” IEEE transactions on pattern analysis and machine intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.
  • [21] A. Samal and P. A. Iyengar, “Automatic recognition and analysis of human faces and facial expressions: A survey,” Pattern recognition, vol. 25, no. 1, pp. 65-77, 1992.
  • [22] D. Dumn, “Using a multi-layer perceptron neural for human voice identification,” Proc. - 4th Int. Conf. Signal Process. Applicat. Technol., 1993.
  • [23] L. Biel, O. Pettersson, L. Philipson and P. Wide, “ECG analysis: a new approach in human identification,” IEEE Transactions on Instrumentation and Measurement, vol. 50, no. 3, pp. 808-812, 2001.
  • [24] S. Mayya, V. Jilla, V. N. Tiwari, M. M. Nayak and R. Narayanan, " Continuous monitoring of stress on smartphone using heart rate variability," Proc. - 15th IEEE International Conference on Bioinformatics and Bioengineering, pp. 1-5, 2015.
  • [25] G. Lu, F. Yang, J. A. Taylor and J. F. Stein, " A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects," Journal of medical engineering & technology, vol. 33, no. 8, pp. 634- 641, 2009.
  • [26] N. Selvaraj, A. Jaryal, J. Santhosh, K. K. Deepak and S. Anand, "Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography," Journal of medical engineering & technology, vol. 32, no. 6, pp. 479-484, 2008.
  • [27] J. Taelman, S. Vandeput, A. Spaepen ans S. Van Huffel, "Influence of mental stress on heart rate and heart rate variability," Proc. - 4th European conference of the international federation for medical and biological engineering, pp. 1366-1369, 2009.
  • [28] W. Wu and J. Lee, "Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment," Proc. - 10th IEEE International Symposium on Signal Processing and Information Technology, pp. 144-149, 2010.
  • [29] W. Wu, J. Lee and H. Chen, " Estimation of heart rate variability changes during different visual stimulations using non-invasive continuous ecg monitoring system,” Proc. - International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp. 344-347, 2009.
  • [30] D. McDuff, S. Gontarek and R. Picard, "Remote measurement of cognitive stress via heart rate variability," Proc. - 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2957-2960, 2014.
  • [31] C. Wang and F. Wang, "An emotional analysis method based on heart rate variability," Proc. - IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 104-107, 2012.
  • [32] P. B. Laursen, A. Said and B. Martin, "Nocturnal heart rate variability following supramaximal intermittent exercise," International Journal of Sports Physiology and Performance, vol. 4, no. 4, pp. 435-447, 2009.
  • [33] P. J. Millar, M. Rakobowchuk, N. Mccartney and M. J. Macdonald, " Heart rate variability and nonlinear analysis of heart rate dynamics following single and multiple Wingate bouts," Applied Physiology, Nutrition & Metabolism, vol. 34, no. 5, pp. 875-883, 2009.
  • [34] O. F. Barak, D. G. Jakovljevic, J. Z. P. Gacesa, Z. B. Ovcin, D. A. Brodie and N. G. Grujic, " Heart rate variability before and after cycle exercise in relation to different body positions," Journal of sports science & medicine, vol. 9, no. 2, pp. 176-182, 2010.
  • [35] U. Wiklund, M. Karlsson, M. Oöström and T. Messner, " Influence of energy drinks and alcohol on post‐exercise heart rate recovery and heart rate variability," Clinical physiology and functional imaging, vol. 29, no. 1, pp. 74-80, 2009.
  • [36] D. Aras, F. Akça and C. Akalan, "50 metre sprint yüzmenin 13-14 yaşlarındaki erkek yüzücülerde kalp hızı değişkenliğine etkisi," Spormetre Beden Eğitimi ve Spor Bilimleri Dergisi, vol. 11, no. 1, pp. 13-18, 2013.
  • [37] R. Ulu, N. Gözel, İ. P. Yiğit, Z. Kemeç, K. A. Uğur, O. Doğdu and A. Doğukan, " Diyaliz Modalitelerinin Kalp Hızı Değişkenliği Üzerine Olan Etkisi," Türk Nefroloji Diyaliz ve Transplantasyon Dergisi, vol. 26, no. 1, pp. 93-97, 2017.
  • [38] A. F. Aleixo, E. G. Lima, É. C. Leite, A. V. Inocêncio, L. T. Lins and M. A. Rodrigues, "Wearable Device for Acquisition of SpO 2 and Heart Rate," Proc. - XXVI Brazilian Congress on Biomedical Engineering, pp. 577-582, 2019.
  • [39] M. Paul, A. F. Mota, C: H. Antink, V. Blazek and S. Leonhardt, " Modeling photoplethysmographic signals in camera-based perfusion measurements: optoelectronic skin phantom," Biomedical optics express, vol. 10, no. 9, pp. 4353-4368, 2019.
  • [40] J. Přibil, A. Přibilová and I. Frollo, "Comparative Measurement of the PPG Signal on Different Human Body Positions by Sensors Working in Reflection and Transmission Modes," In Engineering Proceedings, vol. 2, no. 69, pp. 1-7, 2020.
  • [41] Ö. Yıldırım, Kalp aritmisinin çift dalgaboylu PPG sinyalleri kullanılarak belirlenmesi, Master Thesis, Dept. of Electrical and Electronics Engineering, Graduate Institute of Natural and Applied Sciences, Karadeniz Teknik University, Trabzon, Turkey, 2017.
  • [42] D. S. Goldstein, R. McCarty, R. J. Polinsky and I. J. Kopin, "Relationship between plasma norepinephrine and sympathetic neural activity," Hypertension, vol. 5, no. 4, pp. 552-559, 1983.
  • [43] B. G. Wallin and N. Charkoudian, " Sympathetic neural control of integrated cardiovascular function: insights from measurement of human sympathetic nerve activity," Muscle & nerve, vol. 36, no. 5, pp. 595-614, 2007.
  • [44] T. B. Kuo and C. C. Yang, "Altered frequency characteristic of central vasomotor control in SHR," American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 1, pp. H201-H207, 2000.
  • [45] A. H. Marques, M. N. Silverman and E. M. Sternberg, "Evaluation of stress systems by applying noninvasive methodologies: measurements of neuroimmune biomarkers in the sweat, heart rate variability and salivary cortisol," Neuroimmunomodulation, vol. 17, no. 3, pp. 205-208, 2010.
  • [46] R. Freeman and A. L. Komaroff, "Does the chronic fatigue syndrome involve the autonomic nervous system?" The American journal of medicine, vol. 102, no. 4, pp. 357-364, 1997.
  • [47] J. W. Hughes and C. M. Stoney, " Depressed mood is related to high-frequency heart rate variability during stressors," Psychosomatic medicine, vol. 62, no. 6, pp. 796- 803, 2000.
  • [48] T. Chandola, A. Heraclides and M. Kumari, " Psychophysiological biomarkers of workplace stressors," Neuroscience & Biobehavioral Reviews, vol. 35, no. 1, pp. 51-57, 2010.
  • [49] S. Açıkgöz and E. Diker, "Kalp hızı değişkenliği," MN Kardiyoloji, vol. 3, no. 1, pp. 275-278, 1996.
  • [50] "MAX30100 Nabız ve Kalp Atış Hızı Sensör Modülü," 2021. [Online]. Available: https://www.direnc.net/max30100-nabiz-ve-kalp-atis-hizi-sensor-modulu. [Accessed: 24-June-2021].
  • [51] "Maxim Integrated MAX30100 Datasheet," 2021. [Online]. Available: https://datasheets.maximintegrated.com/en/ds/MAX30100.pdf. [Accessed: 24-June-2021].
  • [52] R. A. Peura, "Chapter 7. Blood Pressure and Sound," 2014. [Online]. Available: http://slideplayer.com/slide/4156539/ [Accessed: 24-June-2021].
  • [53] D. Yılmaz and B. Çiğdem B, "Epilepsi Hastalarında Levetirasetam Tedavisinin Otonom Sinir Sistemi Fonksiyonları Üzerine Etkileri," Epilepsi, vol. 26, no. 2, pp. 81-87, 2020.
  • [54] K. L. Chong, D. Holden and T. Olin, "Heart Rate Monitor," 2010. [Online]. Available: http://www.academia.edu/7884606/Heart_Rate_Monitor [Accessed: 24-June-2021].
  • [55] G. Aydın and O. Özhan, "Pulse Oksimetre Tasarım Ve Analizinin Yapılması", Proc. - 2. Ulusal Biyomedikal Cihaz Tasarımı ve Üretimi Sempozyumu, pp. 46-48, 2017.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Makaleler
Yazarlar

Ziynet Pamuk 0000-0003-3792-2183

Ceren Kaya 0000-0002-1970-2833

Proje Numarası 2020-39971044-02
Yayımlanma Tarihi 30 Nisan 2022
Gönderilme Tarihi 16 Kasım 2021
Kabul Tarihi 26 Nisan 2022
Yayımlandığı Sayı Yıl 2022Cilt: 5 Sayı: 1

Kaynak Göster

IEEE Z. Pamuk ve C. Kaya, “Detection of Heart Rate Variability from Photoplethysmography (PPG) Signals Obtained by Raspberry Pi Microcomputer”, SAUCIS, c. 5, sy. 1, ss. 104–120, 2022, doi: 10.35377/saucis...1024414.
 Kapak Resmi İndir

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